Most research in learning for planning has concentrated on efficiency gains. Another important goal is improving the quality of final plans. Learning to improve plan quality has been examined by a few researchers, however, little research has been done learning to improve both efficiency and quality. This paper explores this problem by using the SCOPE learning system to acquire control knowledge that improves on both of these metrics. Since SCOPE uses a very flexible training approach, we can easily focus it's learning algorithm to prefer search paths that are better for particular evaluation metrics. Experimental results show that SCOPE can significantly improve both the quality of final plans and overall planning efficiency.